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Usage Information

Combining STING-based neoantigen-targeted vaccine with checkpoint modulators enhances antitumor immunity in murine pancreatic cancer
Heather L. Kinkead, Alexander Hopkins, Eric Lutz, Annie A. Wu, Mark Yarchoan, Kayla Cruz, Skylar Woolman, Teena Vithayathil, Laura H. Glickman, Chudi O. Ndubaku, Sarah M. McWhirter, Thomas W. Dubensky Jr., Todd D. Armstrong, Elizabeth M. Jaffee, Neeha Zaidi
Heather L. Kinkead, Alexander Hopkins, Eric Lutz, Annie A. Wu, Mark Yarchoan, Kayla Cruz, Skylar Woolman, Teena Vithayathil, Laura H. Glickman, Chudi O. Ndubaku, Sarah M. McWhirter, Thomas W. Dubensky Jr., Todd D. Armstrong, Elizabeth M. Jaffee, Neeha Zaidi
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Research Article Oncology

Combining STING-based neoantigen-targeted vaccine with checkpoint modulators enhances antitumor immunity in murine pancreatic cancer

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Abstract

Tumor neoantigens arising from somatic mutations in the cancer genome are less likely to be subject to central immune tolerance and are therefore attractive targets for vaccine immunotherapy. We utilized whole-exome sequencing, RNA sequencing (RNASeq), and an in silico immunogenicity prediction algorithm, NetMHC, to generate a neoantigen-targeted vaccine, PancVAX, which was administered together with the STING adjuvant ADU-V16 to mice bearing pancreatic adenocarcinoma (Panc02) cells. PancVAX activated a neoepitope-specific T cell repertoire within the tumor and caused transient tumor regression. When given in combination with two checkpoint modulators, namely anti–PD-1 and agonist OX40 antibodies, PancVAX resulted in enhanced and more durable tumor regression and a survival benefit. The addition of OX40 to vaccine reduced the coexpression of T cell exhaustion markers, Lag3 and PD-1, and resulted in rejection of tumors upon contralateral rechallenge, suggesting the induction of T cell memory. Together, these data provide the framework for testing personalized neoantigen-based combinatorial vaccine strategies in patients with pancreatic and other nonimmunogenic cancers.

Authors

Heather L. Kinkead, Alexander Hopkins, Eric Lutz, Annie A. Wu, Mark Yarchoan, Kayla Cruz, Skylar Woolman, Teena Vithayathil, Laura H. Glickman, Chudi O. Ndubaku, Sarah M. McWhirter, Thomas W. Dubensky Jr., Todd D. Armstrong, Elizabeth M. Jaffee, Neeha Zaidi

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Usage data is cumulative from December 2024 through December 2025.

Usage JCI PMC
Text version 1,196 352
PDF 176 100
Figure 502 8
Supplemental data 109 47
Citation downloads 207 0
Totals 2,190 507
Total Views 2,697
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Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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